Magnetic resonance (MR) imaging is a known technology that can produce images of the inside of an examination subject without radiation exposure. In a typical MR imaging procedure, the subject is positioned in a strong, static, homogeneous base magnetic field B0 (having a field strength that is typically between about 0.5 Tesla and 3 Tesla) in an MR apparatus, so that the subject's nuclear spins become oriented along the base magnetic field. Radio-frequency (RF) excitation pulses are directed into the examination subject to excite nuclear magnetic resonances, and subsequent relaxation of the excited nuclear magnetic resonances can generate RF signals. Rapidly switched magnetic gradient fields can be superimposed on the base magnetic field, in various orientations, to provide spatial coding of the RF signal data. The RF signal data can be detected and used to reconstruct images of the examination subject. For example, the acquired RF signal data are typically digitized and stored as complex numerical values in a k-space matrix. An associated MR image can be reconstructed from the k-space matrix populated with such values using a multi-dimensional Fourier transformation.
Multi-band (MB) magnetic resonance imaging is a relatively recent technique that can accelerate imaging acquisition and therefore improve imaging efficiency. In MB imaging, multiple spatially distributed imaging slices are excited and then acquired simultaneously by using multi-banded radiofrequency (RF) pulses, and the acquired superimposed signals from multiple slices can be ‘unwrapped’ via anti-aliasing reconstruction. An MB factor ‘N’ can be defined as the number of separate slices simultaneously excited, from which image data is obtained.
In a typical MB imaging procedure, a plurality of N adjacent “bands,” or thick slabs, that include the volume to be imaged are defined. This number of slabs is equal to the MB factor. For each excitation/acquisition sequence, one slice within each band is excited substantially simultaneously using an RF pulse sequence, and RF image data is then detected from the excited slices and processed/unwrapped to generate a separate image of each slice. Subsequent MB excitation/acquisition sequences can then be performed, in which further slices are excited, one slice within each band, and further RF image data is detected. This procedure can be continued until all n slices within each of the N slabs have been imaged. Because the k-space lines can be fully sampled during an MB imaging procedure, the simultaneously acquired slices do not exhibit typical √{square root over (N)} reductions in signal-to-noise ratio (SNR) that are typically observed with conventional parallel imaging acceleration techniques.
The simultaneous acquisition of multiple slices in a multi-band imaging procedure can greatly reduce total imaging acquisition time for imaging procedures such as, e.g., whole brain applications with EPI. For example, when imaging a volume of N slabs (where N is the MB factor), with each containing n slices, a single-slice imaging procedure would require n*N separate slice excitation/acquisition sequences. In contrast, by using multi-band imaging, a series of only n sequences are required to image the same overall volume, resulting in a reduction factor for the total imaging time of about 1/N. For example, a MB factor of 6 (corresponding to 6 slices excited simultaneously in each sequence) would need only about ⅙ the time required to image the same number of slices one at a time using a conventional single-band (SB) procedure.
MB magnetic resonance imaging can improve imaging results in certain imaging procedures such as, e.g., whole brain ASL perfusion studies, where high in-plane and through-plane resolution is desired and a large number of thin imaging slices may be needed to achieve the desired coverage. Total imaging time is particularly important in ASL studies, as longer times would allow additional tagged/labeled blood to flow into a volume being imaged, and also allow the excitation of such tagged blood to decay further, both factors reducing the efficacy of the resulting perfusion images. MB imaging can facilitate both higher resolutions and shorter acquisition times to generate more accurate perfusion images.
MB imaging was first implemented for a gradient recalled (GRE) imaging method as described, e.g., in D. J. Larkman et al., Use of multicoil arrays for separation of signal from multiple slices simultaneously excite, J Magn Reson Imaging, 2001, 13(2): p. 313-7. MB imaging was later applied with the Controlled Aliasing in Parallel Imaging Results in Higher Acceleration (CAPIRIHANA) strategy to control aliasing for other imaging methods as described, e.g., in F. A. Breuer et al., Controlled aliasing in parallel imaging results in higher acceleration (CAIPIRINHA) for multi-slice imaging, Magn Reson Med, 2005, 53(3): p. 684-91. Such other imaging methods include, e.g., echo planar imaging (EPI) and steady-state free procession and spin echo imaging. For example, multi-band EPI (MB-EPI) has been demonstrated successfully in functional magnetic resonance imaging (fMRI) and diffusion-weighted imaging (DWI) acquisitions as described, e.g., in K. Setsompop et al., Improving diffusion MRI using simultaneous multi-slice echo planar imaging, Neuroimage, 2012, 63(1): p. 569-80. Recently, MB-EPI has also been compared to single-shot 3D GRASE for brain perfusion imaging using a pulsed arterial spin labeling (PASL) method, namely flow-sensitive alternating inversion recovery (FAIR) in D. A. Feinberg et al., Arterial spin labeling with simultaneous multi-slice echo planar imaging, Magn Reson Med, 2013, 70(6): p. 1500-6.
However, MB imaging can introduce additional signal artifacts, which can affect image quality and may impair the benefits of MB in quantitative imaging applications, such as arterial spin labeling (ASL). These additional artifacts include, e.g., amplified thermal noise arising from coil-sensitivity-based SENSE, GRAPPA, or slice-GRAPPA imaging reconstruction, as reflected by the geometry factor (g-factor). Leakage contamination for measured imaging signals resulted from imperfect anti-aliasing reconstruction algorithms is another type of MB-related artifact. Increased magnetization transfer (MT) effects can also be introduced by MB imaging, arising from a simultaneous application of multiple off-resonance RF pulses during imaging acquisition Such MT effects can be indicated by observed contrast changes between single-band (SB) and MB images. Both the g-factor and leakage contamination levels tend to increase with MB factor, and can be dependent on the applied MB acceleration factors as well as on other MB-specific parameters, such as the GRAPPA or slice-GRAPPA kernel size and field of view (FOV) shift factor.
MB imaging also may limit protocol flexibility/operability in practice. For example, in clinical practice or neuroscience research, imaging protocols typically have a fixed coverage large enough to accommodate the majority of subjects. The strategy of prescribing imaging slices is also usually predetermined, e.g., imaging slices that are angled and/or centered to the AC (anterior commissure)-PC (Posterior commissure) line for neuroimaging. For traditional imaging methods, the number of imaging slices can be freely adjusted according to the size of a particular subject's specific organs. However, due to the intrinsic characteristics of MB imaging, the number of imaging slices is required to be an integer multiple of the MB factor. This can lead to difficulties in changing the number of imaging slices across subjects, especially in cases where high MB acceleration factors (e.g. MB factor 8) are used, where an intermediate number of imaging slices may be needed for sufficient coverage without changing the MB acceleration factor in the protocol.
To evaluate the contamination due to signal leakage resulting from imperfect slice un-aliasing during MB reconstruction, the leakage factor (LF) was proposed as a metric to evaluate the residual aliasing from one slice into other simultaneously acquired slices. To determine the LF, different methods have been proposed and demonstrated, e.g., based on a single representative acquired volume with single band imaging for a given coil and anatomy followed by simulation either using a Frequency Modulation/Monte Carlo (FMMC) method or a Linear System Leakage Approach (LSLA). However, the LF does not directly reflect the total leakage contamination from all other simultaneously acquired imaging slices on a single slice, which is arguably a more meaningful metric when trying to assess the impact of signal contamination relative to a desired signal.
Additionally, in contrast to traditional imaging methods, acquiring unnecessary inferior or superior slices with MB techniques can produce artifacts in simultaneously acquired slices because of imperfect slice anti-aliasing, resulting in additional signal leakage (i.e., image contamination) compared to a procedure in which such unnecessary slices are excluded. Furthermore, unnecessary imaging slices can also interfere with functional imaging acquisitions. For example, in ASL imaging, additional inferior imaging slices can overlap geometrically with the labeling plane, and when minimal TR is used for imaging, the saturation effects of these overlapped imaging slice acquisition can adversely affect spin preparation that is a feature of this quantitative technique.
In conventional MB imaging, a single set of fixed parameters/configurations is used for both imaging acquisition and reconstruction, and these parameters/configurations include RF pulses and gradient waveform settings, imaging readout options, MB factor, Generalized Auto-calibrating Partially Parallel Acquisition (GRAPPA) kernel size, and slice FOV shift factor that can be used in a conventional CAPIRIHANA technique. The use of fixed parameters/configurations for MB acquisition and reconstruction limits the ability to achieve optimal performance in terms of reducing g-factor, decreasing leakage contamination and decreasing acquisition flexibility. For example, to avoid MB-EPI reconstruction failures in specific brain regions, a single large slice-GRAPPA kernel size of 5 or 7 must be used for all MB imaging slices, instead of only using this kernel for those slices that failed reconstruction. Thus, the increase of point-spread function resulting from a large kernel is shared by all slices, thereby increasing image blurring and reducing reconstruction speeds.
Accordingly, it would be desirable to have a system and method for multi-band (MB) magnetic resonance imaging that addresses some of the shortcomings described above, and that may further optimize MB protocols and provide improved image quality, e.g., in brain, spine, musculoskeletal, body, and cardiac imaging applications.